使用生成对抗网络快速模拟SHiP实验中产生的μ子
Fast simulation of muons produced at the SHiP experiment using Generative Adversarial Networks
摘要：
本文提出了一种快速模拟SPS质子束与SHiP实验靶相互作用产生μ介子的方法。SHiP实验将能够寻找400~GeV $/c$ SPS质子束流收集器中产生的新的长寿命粒子，这些粒子的传播距离在50米到几十公里之间。SHiP探测器需要在超低背景条件下工作，需要大量模拟μ子诱导背景过程的样本。通过使用生成对抗网络，可以模拟400~GeV $/c$质子束与SHiP靶相互作用的模拟，否则计算密集的过程。对于SHiP实验的模拟要求，生成网络能够近似密集固定目标的完整模拟，提供$\mathcal{O}(10^6)$的因子的速度增加。为了评估这种方法的性能，比较重建μ介子动量的分布在SHiP的光谱仪之间的样本使用全模拟和生成模型产生的样本。本文讨论的方法可以推广和应用到任何非离散多维分布建模。
英文摘要：
This paper presents a fast approach to simulating muons produced in interactions of the SPS proton beams with the target of the SHiP experiment. The SHiP experiment will be able to search for new long-lived particles produced in a 400~GeV$/c$ SPS proton beam dump and which travel distances between fifty metres and tens of kilometers. The SHiP detector needs to operate under ultra-low background conditions and requires large simulated samples of muon induced background processes. Through the use of Generative Adversarial Networks it is possible to emulate the simulation of the interaction of 400~GeV$/c$ proton beams with the SHiP target, an otherwise computationally intensive process. For the simulation requirements of the SHiP experiment, generative networks are capable of approximating the full simulation of the dense fixed target, offering a speed increase by a factor of $\mathcal{O}(10^6)$. To evaluate the performance of such an approach, comparisons of the distributions of reconstructed muon momenta in SHiP's spectrometer between samples using the full simulation and samples produced through generative models are presented. The methods discussed in this paper can be generalised and applied to modelling any non-discrete multi-dimensional distribution.
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